This document contains code to fit the waggle dance model to each of our 20 different sites. The code here uses the wagglefit package, as well as some additional code stored in fit_data.R to help simplify things. Each site has its own section, with reused code. The final sections create map plots and summary statistic plots.

Optimising each site

# set waggle dance duration in seconds as foraging distance for analysis
data <- read.csv("data/FullHBForagingData.csv")

alldata <- data %>%
  filter(Year == 2017) %>% # remove pilot data conducted in 2016
  select(date, site, duration.seconds) %>%
  rename(foraging_distance = duration.seconds)

BEL

Provides a good fit on the data. All parameters look central and nicely covered.


target_site <- "BEL"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 0.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 8, -5.5, -1.8)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
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all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
BEL collective -181.4148 0.6475794 7.041850 0.8144976 0.0514862 0.1698127 5 372.8296 0.0980392 0.714
BEL individual -183.5011 1.0000000 4.495285 NA 0.0725148 NA 2 371.0021 0.0882353 0.798

all_sites[[target_site]]$fit

BFI

All parameters look central in the likelihood space and a nice fit is returned.


target_site <- "BFI"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 0.6)
bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.2, 3.5, -6.5, -2.)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
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all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
BFI collective -190.4990 0.0448114 0.000001 2.712405 0.1645321 0.2509062 5 390.9980 0.0873362 0.344
BFI individual -211.2967 1.0000000 9.470271 NA 0.1000000 NA 2 426.5934 0.1834061 0.002

all_sites[[target_site]]$fit

BLO

The individual fit isnt great and the Bs paramater is increasing up to the boundry, indicating it can only really take on a straight line / exponential fit. The parameters for the collective model are fairly central in the likelihood space, the fit looks very good.


target_site <- "BLO"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-10, 5)
br_bnds <- c(1.0e-10, 5)
as_bnds <- c(1.0e-5, 5)
ar_bnds <- c(1.0e-10, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 5505)
as_bnds <- c(1.0e-12, 0.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.2, 9.5, -8., -2.2)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
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all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
BLO collective -221.0260 0.2085372 1.785832 0.570999 0.0677377 0.3591282 5 452.0519 0.0705882 0.768
BLO individual -237.3888 1.0000000 5505.000000 NA 0.0001222 NA 2 478.7776 0.1117647 0.215

all_sites[[target_site]]$fit

BUR

Bs and Br go in oposite directions. E.g. Bs approaches 0 whilst Br approaches an every higher number.

Fit looks good.


target_site <- "BUR"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 500)
br_bnds <- c(1.0e-6, 500)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 0.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4, -6.8, -2.5)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords,
  xlims = c(0, 6)
)
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all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
BUR collective -118.3048 0.1116261 1e-06 499.0741 0.1513544 0.0275011 5 246.6096 0.0670732 0.829
BUR individual -143.7177 1.0000000 1e+01 NA 0.1000000 NA 2 291.4355 0.1768293 0.016

all_sites[[target_site]]$fit

CAD

The collective model roughly follows the individual model but is able to acheive a slightly improved fit to the shoulder, hence it provides a higher likelihood score. The AIC indicates this is overfitting, suggesting the individual model provides a more parsimonious explanation.

\(p\) does not approach 1 as one might expect, indicating there is overfitting and so a comparison with an individual model is required.


target_site <- "CAD"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 2)
as_bnds <- c(1.0e-12, 0.8)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 1.8, -6, -1.2)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
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all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
CAD collective -48.27822 0.164847 1e-06 0.425631 1.4569294 0.3860464 5 106.5564 0.0821918 0.949
CAD individual -49.95313 1.000000 1e-06 NA 0.3854731 NA 2 103.9063 0.1095890 0.745

all_sites[[target_site]]$fit

GIL

The collective model provides the most parsimonious and best fit.


target_site <- "GIL"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 0.8)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 2.1, -8., -2)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
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all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
GIL collective -74.54143 0.2614397 0.000001 1e-06 0.3342945 0.7547164 5 159.0829 0.1354167 0.053
GIL individual -102.30919 1.0000000 2.036505 NA 0.3450190 NA 2 208.6184 0.1979167 0.002

all_sites[[target_site]]$fit

HER

The collective model provides the best fit to the data, but the proportion of scouts is high.


target_site <- "HER"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 100)
br_bnds <- c(1.0e-6, 100)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 2)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4, -6.5, -1.5)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
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all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
HER collective -136.9873 0.249628 1e-06 0.2155781 0.3956663 0.181939 5 283.9745 0.0674157 0.975
HER individual -138.9302 1.000000 1e-06 NA 0.1751111 NA 2 281.8604 0.1123596 0.565

all_sites[[target_site]]$fit

HHS

Collective model provides the best fit but falls under the tail and shoulder. The individual model strugles to find a good fit, probably due to the tail.


target_site <- "HHS"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 50)
br_bnds <- c(1.0e-6, 50)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4, -6.5, -2.2)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
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all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
HHS collective -61.34799 0.0953466 0.000001 1e-06 0.1411228 0.5028644 5 132.6960 0.1333333 0.364
HHS individual -83.74915 1.0000000 7.095173 NA 0.1251512 NA 2 171.4983 0.2444444 0.006

all_sites[[target_site]]$fit

HOR

Collective model provides a very good fit, whilst the individual model fails to find much traction. The proportion of scouts goes very low (~3%) suggesting the majority of the colony are following a small number of scouting individuals.


target_site <- "HOR"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 3, -9, -2.5)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
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all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
HOR collective -40.50196 0.0557038 0.000001 0.5393193 0.2182387 0.6281992 5 91.00392 0.0533333 0.965
HOR individual -56.65192 1.0000000 7.774875 NA 0.2019358 NA 2 117.30385 0.1666667 0.032

all_sites[[target_site]]$fit

MAK

Again the collective model provides a good fit to the data, however, the individual model fits poorly, reducing to an exponential.


target_site <- "MAK"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 20)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# run individual model
individual_result <- fit_individual_model_to_data(data, bounds)
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# view individual model likelihood space to check bounds look ok
individual_result$llspace


# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4.2, -6.5, -2.5)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
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all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
MAK collective -84.51875 0.2093454 2.319365 0.0201563 0.1221729 0.4776196 5 179.0375 0.0909091 0.776
MAK individual -98.02457 1.0000000 9.168368 NA 0.0961027 NA 2 200.0491 0.1919192 0.044

all_sites[[target_site]]$fit

MEL

Collective model provides the best fit, however it misses a large section of the shoulder for the tail. The \(bs\) parameter wants to go to zero, however when let go bellow 1e-6 the behaviour becomes very erratic and the fit deteriorates.


target_site <- "MEL"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 3., -7, -2.1)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
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#> [1] "Itteration 5"
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#> [1] "Itteration 7"
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#> [1] "Itteration 9"
#> [1] "Itteration 10"
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#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
MEL collective -115.3164 0.0806865 1e-06 1e-06 0.2128244 0.3377644 5 240.6327 0.1626016 0.088
MEL individual -135.7308 1.0000000 1e-06 NA 0.2520904 NA 2 275.4617 0.2439024 0.001

all_sites[[target_site]]$fit

MPA

Collective provides the best fit.


target_site <- "MPA"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4., -7.2, -2.1)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
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#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"
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#> [1] "Itteration 2"
#> [1] "Itteration 3"
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#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
MPA collective -181.8729 0.5669623 0.5783197 1e-06 0.1853651 0.4199909 5 373.7458 0.0666667 0.903
MPA individual -188.0378 1.0000000 1.4738536 NA 0.1866656 NA 2 380.0756 0.1000000 0.397

all_sites[[target_site]]$fit

ROT

Colletive provides the best fit.


target_site <- "ROT"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 2., -5.5, -2.1)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
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#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"
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#> [1] "Itteration 2"
#> [1] "Itteration 3"
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#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
ROT collective -138.1293 0.309561 1e-06 1e-06 0.3151219 0.4832754 5 286.2585 0.1546392 0.018
ROT individual -153.3550 1.000000 1e-06 NA 0.3538107 NA 2 310.7100 0.2010309 0.002

all_sites[[target_site]]$fit

SAU

Collective provides the best fit.


target_site <- "SAU"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 2.5, -6.5, -2.1)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
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#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
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#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
SAU collective -108.1850 0.3096488 0.000001 10 0.2620339 0.1638241 5 226.3700 0.0606061 0.946
SAU individual -115.5025 1.0000000 1.488528 NA 0.2710389 NA 2 235.0051 0.1287879 0.197

all_sites[[target_site]]$fit

SOM

Collective provides the best fit but misses a large section of the shoulder.


target_site <- "SOM"

# subset data for target site
data <- alldata %>%
  filter(site == target_site) %>%
  filter(foraging_distance < 6) # remove outlier distance

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 2., -5.5, -2.1)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
SOM collective -70.53597 0.0020792 1e+01 10 1.5000000 0.1486462 5 151.0719 0.1071429 0.492
SOM individual -76.67133 1.0000000 1e-06 NA 0.3772311 NA 2 157.3427 0.1607143 0.087

all_sites[[target_site]]$fit

SRA

Collective provides the best fit.


target_site <- "SRA"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 3., -7.5, -2.1)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
SRA collective -123.3686 0.2424498 0.000001 0.205498 0.2128967 0.492155 5 256.7373 0.0519481 0.982
SRA individual -138.1142 1.0000000 3.002015 NA 0.2133652 NA 2 280.2284 0.1428571 0.082

all_sites[[target_site]]$fit

STU

Individual provides the best fit. Again, the proportion of scouts does not move towards 1. I.e. the collective model fails to reduce to the individual in the MLE fit.


target_site <- "STU"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4, -6.5, -1.5)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
STU collective -155.7499 0.7988692 0.4228714 0.3278325 0.1638306 0.5 5 321.4997 0.0454545 0.999
STU individual -156.5403 1.0000000 1.3465643 NA 0.1612383 NA 2 317.0806 0.0545455 0.997

all_sites[[target_site]]$fit

SWP

Collective provides the best fit.


target_site <- "SWP"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 15)
br_bnds <- c(1.0e-6, 15)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 2., -6, -1.5)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
SWP collective -40.42382 0 2.706000 1.274713 0.2695819 0.3841755 5 90.84765 0.1000000 0.731
SWP individual -46.44077 1 0.000001 NA 0.4236173 NA 2 96.88154 0.1444444 0.260

all_sites[[target_site]]$fit

YAL

Collective provides the best fit.


target_site <- "YAL"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4., -8, -2.)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
YAL collective -205.7605 0.0786728 0.0000010 0.3007459 0.1812311 0.2730218 5 421.5211 0.0439560 0.991
YAL individual -222.4328 1.0000000 0.2168113 NA 0.2128333 NA 2 448.8656 0.0989011 0.318

all_sites[[target_site]]$fit

ZSL

Collective provides the best fit.


target_site <- "ZSL"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 50)
as_bnds <- c(1.0e-12, 1.3)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 1.8, -8, -2)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
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#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"
#> [1] "Itteration 1"
#> [1] "Itteration 2"
#> [1] "Itteration 3"
#> [1] "Itteration 4"
#> [1] "Itteration 5"
#> [1] "Itteration 6"
#> [1] "Itteration 7"
#> [1] "Itteration 8"
#> [1] "Itteration 9"
#> [1] "Itteration 10"

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)
site model loglikelihood p bs br as ar k AIC ks_statistic ks_pvalue
ZSL collective -109.9979 0.2571773 0.0000010 0.8022074 0.3868084 0.5 5 229.9958 0.0361991 1.000
ZSL individual -119.9481 1.0000000 0.3918133 NA 0.4194463 NA 2 243.8962 0.0950226 0.275

all_sites[[target_site]]$fit

Overall findings

# save the analysis results for all sites
# save(all_sites, file = "results/site_fit_results.Rdata")

# group all site results together
df <- map(all_sites, 1) %>%
  bind_rows()

# save results
# saveRDS(df, file = "results/site_fit_results.Rda")

# all_sites$BFI$fit <- all_sites$BFI$fit +
#   ylim(0, 6) +
#   scale_x_continuous(breaks = seq(0, 6, by = 2))

# AIC plot
aic_plot <- df %>%
  group_by(site) %>%
  slice(which.min(AIC)) %>%
  select(model) %>%
  group_by(model) %>%
  summarise(lowest_AIC = n()) %>%
  ggplot(aes(x = model, y = lowest_AIC)) +
  geom_bar(stat = "identity") +
  labs(x = "Model", y = "Count") +
  scale_y_continuous(breaks = seq(0, 20, by = 2)) +
  theme(
    text = element_text(size = 42)
  )

ggsave(
  plot = aic_plot,
  filename = "results/figures/AIC_plot.png",
  width = 90,
  height = 110,
  units = "mm",
  dpi = 300
)

# ks plot
ks_plot_dist <- df %>%
  ggplot(aes(x = ks_pvalue)) +
  geom_histogram(bins = 10, binwidth = 0.1, col = "white") +
  geom_vline(xintercept = 0.05, color = "red", linetype = "dashed") +
  scale_y_continuous(breaks = seq(0, 12, by = 2)) +
  labs(x = "KS P value") +
  facet_wrap(~model, nrow = 3) +
  theme(
    text = element_text(size = 42),
    strip.background = element_blank()
  )


ggsave(
  plot = ks_plot_dist,
  filename = "results/figures/sites_ks.png",
  width = 86,
  height = 180,
  units = "mm",
  dpi = 300
)

ggsave(
  plot = all_sites$STU$fit,
  filename = "results/figures/STU.png",
  width = 90,
  height = 110,
  units = "mm",
  dpi = 300
)

ggsave(
  plot = all_sites$BFI$fit,
  filename = "results/figures/BUR.png",
  width = 90,
  height = 110,
  units = "mm",
  dpi = 300
)

Map plots

Code to make the individual mal plots. These figures are created standalone but are combined in to facets manually in an image processor.


library(ggplot2)
library(ggrepel)
library(gridExtra)
library(ggsn)
library(sf)
library(rworldmap)
library(ggspatial)
library(rnaturalearth)
library(rnaturalearthdata)


full_data_path <- "data/FullHBForagingData.csv"
data_raw <- tibble(read.csv(full_data_path))

map_data <- data_raw %>%
  select(
    site, lat, lon
  ) %>%
  distinct()

map_data <- df %>%
  group_by(site) %>%
  slice(which.min(AIC)) %>%
  select(site, model) %>%
  left_join(map_data, on = "site") %>%
  mutate(col = ifelse(site %in% c("STU", "BUR"), "1", "0"))

locations <- st_as_sf(
  map_data,
  coords = c("lon", "lat"), crs = 4326
)

# Extract selected sites for figure
selected_sites <- filter(map_data, site %in% c("STU", "BUR")) %>%
  mutate(
    label = ifelse(site == "STU", "c", "d")
  )

points_area <- st_bbox(locations)

worldmap <- ne_countries(scale = "large", returnclass = "sf")

# london area
london <- st_read(
  "shapefiles/London_Ward.shp"
)
#> Reading layer `London_Ward' from data source 
#>   `/home/joe/Documents/phd/HoneybeeResearch/wagglefit/analysis/shapefiles/London_Ward.shp' 
#>   using driver `ESRI Shapefile'
#> Simple feature collection with 649 features and 0 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 503568.2 ymin: 155850.8 xmax: 561957.5 ymax: 200933.9
#> Projected CRS: OSGB 1936 / British National Grid

london <- st_transform(
  london,
  CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
)

inset <- ggplot() +
  geom_sf(
    data = worldmap,
    fill = "grey90",
    color = "#4b4949d0"
  ) +
  geom_sf(
    data = london,
    fill = "#ced0cffc",
    lwd = 0
  ) +
  coord_sf(
    xlim = c(points_area[[1]] - 0.1, points_area[[3]] + 0.1),
    ylim = c(points_area[[2]] - 0.1, points_area[[4]] + 0.1)
  ) +
  geom_point(
    data = map_data,
    aes(x = lon, y = lat, shape = model, colour = model), size = 1.5
  ) +
  geom_text(
    data = selected_sites, aes(x = lon, y = lat, label = label),
    nudge_x = c(0, -0.07), nudge_y = c(0.05, 0.05), size = 8
  ) +
  geom_point(
    data = selected_sites, aes(x = lon, y = lat), size = 1.5, shape = 4
  ) +
  annotation_north_arrow(
    location = "tr", which_north = "true",
    style = north_arrow_fancy_orienteering,
    height = unit(15, "mm"),
    width = unit(15, "mm"),
    text_cex = 1.5
  ) +
  annotation_scale(
    location = "br",
    text_cex = 1.5
  ) +
  scale_shape_manual(values = c(1, 2)) +
  scale_colour_manual(values = c("black", "red")) +
  theme_nothing() +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  labs(x = NULL, y = NULL) +
  theme(
    panel.border = element_rect(color = "black", fill = NA, size = .5)
  )

inset


# make full plot
base <- ggplot(data = worldmap) +
  geom_sf(
    fill = "#c4cfc8",
    color = "#4b4949d0",
    lwd = 0.2
  ) +
  coord_sf(
    xlim = c(-11, 3),
    ylim = c(49.5, 60)
  ) +
  # geom_point(data = map_data, aes(x = lon, y = lat), size = 0.2) +
  geom_rect(
    aes(
      xmin = points_area$xmin[[1]] - 0.1, xmax = points_area$xmax[[1]] + 0.1,
      ymin = points_area$ymin[[1]] - 0.1, ymax = points_area$ymax[[1]] + 0.1
    ),
    fill = NA,
    colour = "black",
    size = .02
  ) +
  theme_nothing() +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  labs(x = NULL, y = NULL) +
  theme(
    panel.border = element_rect(color = "black", fill = NA, size = .5)
  )

base


merge_plot <- base +
  annotation_custom(
    ggplotGrob(inset),
    xmin = 1,
    xmax = 13,
    ymin = 52.5,
    ymax = 60
  )

merge_plot


ggsave(
  plot = merge_plot,
  filename = "results/figures/site_map.png",
  width = 90,
  height = 110,
  units = "mm",
  dpi = 300
)


merge_plot_2 <- inset +
  annotation_custom(
    ggplotGrob(base),
    xmin = -3.45,
    xmax = 2.2,
    ymin = 51.01,
    ymax = 51.45
  )

merge_plot_2


sites_model_plot <- plot_grid(
  merge_plot_2, ks_plot_dist, all_sites$STU$fit, all_sites$BUR$fit,
  labels = c("A", "B", "C", "D"), label_size = 24
)

ggsave(
  plot = sites_model_plot,
  filename = "results/figures/sites_model_plot.png",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 300
)

ggsave(
  plot = sites_model_plot,
  filename = "results/figures/sites_model_plot.svg",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 300
)

ggsave(
  plot = sites_model_plot,
  filename = "results/figures/sites_model_plot.pdf",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 300
)

ggsave(
  plot = sites_model_plot,
  filename = "results/figures/sites_model_plot.bmp",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 600
)

model_sites_fits <- plot_grid(
  merge_plot_2, ks_plot_dist,
  labels = c("A", "B"), label_size = 24
)

ggsave(
  plot = model_sites_fits,
  filename = "results/figures/model_sites_fits.png",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 300
)

stu_zsl_fit <- plot_grid(
  all_sites$STU$fit, all_sites$BUR$fit,
  labels = c("A", "B"), label_size = 24
)

ggsave(
  plot = stu_zsl_fit,
  filename = "results/figures/stu_zsl_fit.svg",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 100
)
# other map plots
inset <- ggplot() +
  geom_sf(
    data = worldmap,
    fill = "grey90",
    color = "#4b4949d0"
  ) +
  geom_sf(
    data = london,
    fill = "#ced0cffc",
    lwd = 0
  ) +
  coord_sf(
    xlim = c(points_area[[1]] - 0.1, points_area[[3]] + 0.1),
    ylim = c(points_area[[2]] - 0.1, points_area[[4]] + 0.1)
  ) +
  geom_point(
    data = map_data,
    aes(x = lon, y = lat, shape = model, colour = model), size = 1.5
  ) +
  annotation_north_arrow(
    location = "tr", which_north = "true",
    style = north_arrow_fancy_orienteering,
    height = unit(10, "mm"),
    width = unit(10, "mm")
  ) +
  annotation_scale(
    location = "br",
    text_cex = 3
  ) +
  scale_shape_manual(values = c(1, 2)) +
  scale_colour_manual(values = c("black", "red")) +
  theme_nothing() +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  labs(x = NULL, y = NULL) +
  theme(
    panel.border = element_rect(color = "black", fill = NA, size = .5),
    text = element_text(size = 42)
  )

inset

# make full plot
base <- ggplot(data = worldmap) +
  geom_sf(
    fill = "#c4cfc8",
    color = "#4b4949d0",
    lwd = 0.2
  ) +
  coord_sf(
    xlim = c(-11, 15),
    ylim = c(49.5, 60)
  ) +
  geom_point(data = map_data, aes(x = lon, y = lat), size = 0.2) +
  geom_rect(
    aes(
      xmin = points_area$xmin[[1]] - 0.1, xmax = points_area$xmax[[1]] + 0.1,
      ymin = points_area$ymin[[1]] - 0.1, ymax = points_area$ymax[[1]] + 0.1
    ),
    fill = NA,
    colour = "black",
    size = .02
  ) +
  theme_nothing() +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  labs(x = NULL, y = NULL) +
  theme(
    panel.border = element_rect(color = "black", fill = NA, size = .5)
  )

merge_plot <- base +
  annotation_custom(
    ggplotGrob(inset),
    xmin = 1,
    xmax = 13,
    ymin = 52.5,
    ymax = 60
  )

merge_plot

ggsave(
  plot = merge_plot,
  filename = "results/figures/site_map.png",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 300
)


sites_model_plot <- plot_grid(
  all_sites$STU$fit, all_sites$BUR$fit, aic_plot, ks_plot_dist,
  labels = c("A", "B", "C", "D"), label_size = 42
)

sites_model_plot

ggsave(
  plot = sites_model_plot,
  filename = "results/figures/results_model_plot.png",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 300
)
---
title: Fitting waggle dance models to waggle dance data
author: Joseph Palmer
output:
  html_notebook:
    theme: yeti
    toc: true
    toc_float: true
---

This document contains code to fit the waggle dance model to each of our 20 different sites. The code here uses the wagglefit package, as well as some additional code stored in `fit_data.R` to help simplify things. Each site has its own section, with reused code. The final sections create map plots and summary statistic plots.

```{r, preamble, include = FALSE}
devtools::load_all()

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
library(ggplot2)
theme_set(
  theme_classic() +
    theme(
      text = element_text(family = "DejaVuSerif", size = 48)
    )
)
library(cowplot)
library(dplyr)
library(tibble)
source("fit_data.R")
library(kableExtra)

library(showtext)
showtext_auto()

run_wagglefit_analysis <- function(target_site, data, collective_bounds, individual_bounds, subplot_coords, xlims = NULL) {

  # run collective model
  colletive_result <- fit_collective_model_to_data(data, collective_bounds)

  # run individual model
  individual_result <- fit_individual_model_to_data(data, individual_bounds)

  # make plot of model fits
  full_plot <- make_full_plot(
    data$foraging_distance,
    list(
      "collective" = colletive_result$solution,
      "individual" = individual_result$solution
    ),
    subplot_coords = subplot_coords,
    xlims = xlims
  )

  # calculate ks statistics
  ks_test_result_collective <- calc_ks_boot(
    data$foraging_distance, colletive_result$solution$est, "collective"
  )
  ks_test_result_individual <- calc_ks_boot(
    data$foraging_distance, individual_result$solution$est, "individual"
  )

  # bring results together
  model_fits <- tibble(
    site = c(target_site, target_site),
    model = c("collective", "individual"),
    loglikelihood = c(
      colletive_result$solution$fmax, individual_result$solution$fmax
    ),
    p = c(colletive_result$solution$est[1], 1),
    bs = c(colletive_result$solution$est[2], individual_result$solution$est[1]),
    br = c(colletive_result$solution$est[3], NA),
    as = c(colletive_result$solution$est[4], individual_result$solution$est[2]),
    ar = c(colletive_result$solution$est[5], NA),
    k = c(
      length(colletive_result$solution$est),
      length(individual_result$solution$est)
    ),
    AIC = c(
      calc_aic(
        length(colletive_result$solution$est), colletive_result$solution$fmax
      ),
      calc_aic(
        length(individual_result$solution$est), individual_result$solution$fmax
      )
    ),
    ks_statistic = c(
      ks_test_result_collective$ks$statistic[[1]],
      ks_test_result_individual$ks$statistic[[1]]
    ),
    ks_pvalue = c(
      ks_test_result_collective$ks.boot.pvalue,
      ks_test_result_individual$ks.boot.pvalue
    )
  )

  return(
    list(
      fit_result = model_fits, fit = full_plot,
      individual_llspace = individual_result$llspace,
      collective_llspace = colletive_result$llspace
    )
  )
}

all_sites <- as.list(rep(0, 20))
names(all_sites) <- get_data() %>%
  select(site) %>%
  unique() %>%
  pull()
```

## Optimising each site

```{r}
# set waggle dance duration in seconds as foraging distance for analysis
data <- read.csv("data/FullHBForagingData.csv")

alldata <- data %>%
  filter(Year == 2017) %>% # remove pilot data conducted in 2016
  select(date, site, duration.seconds) %>%
  rename(foraging_distance = duration.seconds)
```

### BEL

Provides a good fit on the data. All parameters look central and nicely covered.

```{r, BEL, message = FALSE, warning = FALSE}

target_site <- "BEL"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 0.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 8, -5.5, -1.8)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```



### BFI

All parameters look central in the likelihood space and a nice fit is returned.

```{r, BFI, message = FALSE, warning = FALSE}

target_site <- "BFI"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 0.6)
bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.2, 3.5, -6.5, -2.)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```


### BLO

The individual fit isnt great and the Bs paramater is increasing up to the boundry, indicating it can only really take on a straight line / exponential fit. The parameters for the collective model are fairly central in the likelihood space, the fit looks very good.

```{r, BLO, message = FALSE, warning = FALSE}

target_site <- "BLO"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-10, 5)
br_bnds <- c(1.0e-10, 5)
as_bnds <- c(1.0e-5, 5)
ar_bnds <- c(1.0e-10, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 5505)
as_bnds <- c(1.0e-12, 0.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.2, 9.5, -8., -2.2)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```


### BUR

Bs and Br go in oposite directions. E.g. Bs approaches 0 whilst Br approaches an every higher number.

Fit looks good.

```{r, BUR, message = FALSE, warning = FALSE}

target_site <- "BUR"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 500)
br_bnds <- c(1.0e-6, 500)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 0.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4, -6.8, -2.5)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords,
  xlims = c(0, 6)
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```


### CAD

The collective model roughly follows the individual model but is able to acheive a slightly improved fit to the shoulder, hence it provides a higher likelihood score. The AIC indicates this is overfitting, suggesting the individual model provides a more parsimonious explanation.

$p$ does not approach 1 as one might expect, indicating there is overfitting and so a comparison with an individual model is required.

```{r, CAD, message = FALSE, warning = FALSE}

target_site <- "CAD"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 2)
as_bnds <- c(1.0e-12, 0.8)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 1.8, -6, -1.2)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```


### GIL

The collective model provides the most parsimonious and best fit.

```{r, GIL, message = FALSE, warning = FALSE}

target_site <- "GIL"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 0.8)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 2.1, -8., -2)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```


### HER

The collective model provides the best fit to the data, but the proportion of scouts is high.

```{r, HER, message = FALSE, warning = FALSE}

target_site <- "HER"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 100)
br_bnds <- c(1.0e-6, 100)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 2)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4, -6.5, -1.5)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```


### HHS

Collective model provides the best fit but falls under the tail and shoulder. The individual model strugles to find a good fit, probably due to the tail.

```{r, HHS, message = FALSE, warning = FALSE}

target_site <- "HHS"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 50)
br_bnds <- c(1.0e-6, 50)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4, -6.5, -2.2)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```


### HOR

Collective model provides a very good fit, whilst the individual model fails to find much traction. The proportion of scouts goes very low (~3%) suggesting the majority of the colony are following a small number of scouting individuals.

```{r, HOR, message = FALSE, warning = FALSE}

target_site <- "HOR"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 1.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 3, -9, -2.5)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```


### MAK

Again the collective model provides a good fit to the data, however, the individual model fits poorly, reducing to an exponential.

```{r, MAK, message = FALSE, warning = FALSE}

target_site <- "MAK"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 20)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# run individual model
individual_result <- fit_individual_model_to_data(data, bounds)

# view individual model likelihood space to check bounds look ok
individual_result$llspace

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4.2, -6.5, -2.5)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```


### MEL

Collective model provides the best fit, however it misses a large section of the shoulder for the tail. The $bs$ parameter wants to go to zero, however when let go bellow 1e-6 the behaviour becomes very erratic and the fit deteriorates.

```{r, MEL, message = FALSE, warning = FALSE}

target_site <- "MEL"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 3., -7, -2.1)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```


### MPA

Collective provides the best fit.

```{r, MPA, message = FALSE, warning = FALSE}

target_site <- "MPA"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4., -7.2, -2.1)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```

### ROT

Colletive provides the best fit.

```{r, ROT, message = FALSE, warning = FALSE}

target_site <- "ROT"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 2., -5.5, -2.1)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```


### SAU

Collective provides the best fit.

```{r, SAU, message = FALSE, warning = FALSE}

target_site <- "SAU"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 2.5, -6.5, -2.1)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```

### SOM

Collective provides the best fit but misses a large section of the shoulder.

```{r, SOM, message = FALSE, warning = FALSE}

target_site <- "SOM"

# subset data for target site
data <- alldata %>%
  filter(site == target_site) %>%
  filter(foraging_distance < 6) # remove outlier distance

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 2., -5.5, -2.1)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```

### SRA

Collective provides the best fit.

```{r, SRA, message = FALSE, warning = FALSE}

target_site <- "SRA"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 3., -7.5, -2.1)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```

### STU

Individual provides the best fit. Again, the proportion of scouts does not move towards 1. I.e. the collective model fails to reduce to the individual in the MLE fit.

```{r, STU, message = FALSE, warning = FALSE}

target_site <- "STU"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4, -6.5, -1.5)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```

### SWP

Collective provides the best fit.

```{r, SWP, message = FALSE, warning = FALSE}

target_site <- "SWP"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 15)
br_bnds <- c(1.0e-6, 15)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 2., -6, -1.5)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```

### YAL

Collective provides the best fit.

```{r, YAL, message = FALSE, warning = FALSE}

target_site <- "YAL"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.1)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 4., -8, -2.)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```

### ZSL

Collective provides the best fit.

```{r, ZSL, message = FALSE, warning = FALSE}

target_site <- "ZSL"

# subset data for target site
data <- alldata %>%
  filter(site == target_site)

# set up bounds for the collective model
p_bnds <- c(0, 1.0)
bs_bnds <- c(1.0e-6, 10)
br_bnds <- c(1.0e-6, 10)
as_bnds <- c(1.0e-12, 1.5)
ar_bnds <- c(1.0e-12, 0.5)
collective_bounds <- rbind(
  p_bnds, bs_bnds,
  br_bnds, as_bnds,
  ar_bnds
)

# set up bounds for the individual model
bs_bnds <- c(1.0e-6, 50)
as_bnds <- c(1.0e-12, 1.3)
individual_bounds <- rbind(
  bs_bnds, as_bnds
)

# set coordinates for histogram subplot
subplot_coords <- c(0.1, 1.8, -8, -2)

all_sites[[target_site]] <- run_wagglefit_analysis(
  target_site, data, collective_bounds, individual_bounds, subplot_coords
)

all_sites[[target_site]]$fit_result %>%
  kbl() %>%
  kable_classic(full_width = F)

all_sites[[target_site]]$fit
```

## Overall findings

```{r, make-plots, message = FALSE, warning = FALSE}
# save the analysis results for all sites
# save(all_sites, file = "results/site_fit_results.Rdata")

# group all site results together
df <- map(all_sites, 1) %>%
  bind_rows()

# save results
# saveRDS(df, file = "results/site_fit_results.Rda")

# all_sites$BFI$fit <- all_sites$BFI$fit +
#   ylim(0, 6) +
#   scale_x_continuous(breaks = seq(0, 6, by = 2))

# AIC plot
aic_plot <- df %>%
  group_by(site) %>%
  slice(which.min(AIC)) %>%
  select(model) %>%
  group_by(model) %>%
  summarise(lowest_AIC = n()) %>%
  ggplot(aes(x = model, y = lowest_AIC)) +
  geom_bar(stat = "identity") +
  labs(x = "Model", y = "Count") +
  scale_y_continuous(breaks = seq(0, 20, by = 2)) +
  theme(
    text = element_text(size = 42)
  )

ggsave(
  plot = aic_plot,
  filename = "results/figures/AIC_plot.png",
  width = 90,
  height = 110,
  units = "mm",
  dpi = 300
)

# ks plot
ks_plot_dist <- df %>%
  ggplot(aes(x = ks_pvalue)) +
  geom_histogram(bins = 10, binwidth = 0.1, col = "white") +
  geom_vline(xintercept = 0.05, color = "red", linetype = "dashed") +
  scale_y_continuous(breaks = seq(0, 12, by = 2)) +
  labs(x = "KS P value") +
  facet_wrap(~model, nrow = 3) +
  theme(
    text = element_text(size = 42),
    strip.background = element_blank()
  )


ggsave(
  plot = ks_plot_dist,
  filename = "results/figures/sites_ks.png",
  width = 86,
  height = 180,
  units = "mm",
  dpi = 300
)

ggsave(
  plot = all_sites$STU$fit,
  filename = "results/figures/STU.png",
  width = 90,
  height = 110,
  units = "mm",
  dpi = 300
)

ggsave(
  plot = all_sites$BFI$fit,
  filename = "results/figures/BUR.png",
  width = 90,
  height = 110,
  units = "mm",
  dpi = 300
)
```


### Map plots


Code to make the individual mal plots. These figures are created standalone but are combined in to facets manually in an image processor.

```{r, map-plots, message = FALSE, warning = FALSE}

library(ggplot2)
library(ggrepel)
library(gridExtra)
library(ggsn)
library(sf)
library(rworldmap)
library(ggspatial)
library(rnaturalearth)
library(rnaturalearthdata)


full_data_path <- "data/FullHBForagingData.csv"
data_raw <- tibble(read.csv(full_data_path))

map_data <- data_raw %>%
  select(
    site, lat, lon
  ) %>%
  distinct()

map_data <- df %>%
  group_by(site) %>%
  slice(which.min(AIC)) %>%
  select(site, model) %>%
  left_join(map_data, on = "site") %>%
  mutate(col = ifelse(site %in% c("STU", "BUR"), "1", "0"))

locations <- st_as_sf(
  map_data,
  coords = c("lon", "lat"), crs = 4326
)

# Extract selected sites for figure
selected_sites <- filter(map_data, site %in% c("STU", "BUR")) %>%
  mutate(
    label = ifelse(site == "STU", "c", "d")
  )

points_area <- st_bbox(locations)

worldmap <- ne_countries(scale = "large", returnclass = "sf")

# london area
london <- st_read(
  "shapefiles/London_Ward.shp"
)

london <- st_transform(
  london,
  CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
)

inset <- ggplot() +
  geom_sf(
    data = worldmap,
    fill = "grey90",
    color = "#4b4949d0"
  ) +
  geom_sf(
    data = london,
    fill = "#ced0cffc",
    lwd = 0
  ) +
  coord_sf(
    xlim = c(points_area[[1]] - 0.1, points_area[[3]] + 0.1),
    ylim = c(points_area[[2]] - 0.1, points_area[[4]] + 0.1)
  ) +
  geom_point(
    data = map_data,
    aes(x = lon, y = lat, shape = model, colour = model), size = 1.5
  ) +
  geom_text(
    data = selected_sites, aes(x = lon, y = lat, label = label),
    nudge_x = c(0, -0.07), nudge_y = c(0.05, 0.05), size = 8
  ) +
  geom_point(
    data = selected_sites, aes(x = lon, y = lat), size = 1.5, shape = 4
  ) +
  annotation_north_arrow(
    location = "tr", which_north = "true",
    style = north_arrow_fancy_orienteering,
    height = unit(15, "mm"),
    width = unit(15, "mm"),
    text_cex = 1.5
  ) +
  annotation_scale(
    location = "br",
    text_cex = 1.5
  ) +
  scale_shape_manual(values = c(1, 2)) +
  scale_colour_manual(values = c("black", "red")) +
  theme_nothing() +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  labs(x = NULL, y = NULL) +
  theme(
    panel.border = element_rect(color = "black", fill = NA, size = .5)
  )

inset

# make full plot
base <- ggplot(data = worldmap) +
  geom_sf(
    fill = "#c4cfc8",
    color = "#4b4949d0",
    lwd = 0.2
  ) +
  coord_sf(
    xlim = c(-11, 3),
    ylim = c(49.5, 60)
  ) +
  # geom_point(data = map_data, aes(x = lon, y = lat), size = 0.2) +
  geom_rect(
    aes(
      xmin = points_area$xmin[[1]] - 0.1, xmax = points_area$xmax[[1]] + 0.1,
      ymin = points_area$ymin[[1]] - 0.1, ymax = points_area$ymax[[1]] + 0.1
    ),
    fill = NA,
    colour = "black",
    size = .02
  ) +
  theme_nothing() +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  labs(x = NULL, y = NULL) +
  theme(
    panel.border = element_rect(color = "black", fill = NA, size = .5)
  )

base

merge_plot <- base +
  annotation_custom(
    ggplotGrob(inset),
    xmin = 1,
    xmax = 13,
    ymin = 52.5,
    ymax = 60
  )

merge_plot

ggsave(
  plot = merge_plot,
  filename = "results/figures/site_map.png",
  width = 90,
  height = 110,
  units = "mm",
  dpi = 300
)


merge_plot_2 <- inset +
  annotation_custom(
    ggplotGrob(base),
    xmin = -3.45,
    xmax = 2.2,
    ymin = 51.01,
    ymax = 51.45
  )

merge_plot_2

sites_model_plot <- plot_grid(
  merge_plot_2, ks_plot_dist, all_sites$STU$fit, all_sites$BUR$fit,
  labels = c("A", "B", "C", "D"), label_size = 24
)

ggsave(
  plot = sites_model_plot,
  filename = "results/figures/sites_model_plot.png",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 300
)

ggsave(
  plot = sites_model_plot,
  filename = "results/figures/sites_model_plot.svg",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 300
)

ggsave(
  plot = sites_model_plot,
  filename = "results/figures/sites_model_plot.pdf",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 300
)

ggsave(
  plot = sites_model_plot,
  filename = "results/figures/sites_model_plot.bmp",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 600
)

model_sites_fits <- plot_grid(
  merge_plot_2, ks_plot_dist,
  labels = c("A", "B"), label_size = 24
)

ggsave(
  plot = model_sites_fits,
  filename = "results/figures/model_sites_fits.png",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 300
)

stu_zsl_fit <- plot_grid(
  all_sites$STU$fit, all_sites$BUR$fit,
  labels = c("A", "B"), label_size = 24
)

ggsave(
  plot = stu_zsl_fit,
  filename = "results/figures/stu_zsl_fit.svg",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 100
)
```


```{r, eval = FALSE, other-map-plots, message = FALSE, warning = FALSE}
# other map plots
inset <- ggplot() +
  geom_sf(
    data = worldmap,
    fill = "grey90",
    color = "#4b4949d0"
  ) +
  geom_sf(
    data = london,
    fill = "#ced0cffc",
    lwd = 0
  ) +
  coord_sf(
    xlim = c(points_area[[1]] - 0.1, points_area[[3]] + 0.1),
    ylim = c(points_area[[2]] - 0.1, points_area[[4]] + 0.1)
  ) +
  geom_point(
    data = map_data,
    aes(x = lon, y = lat, shape = model, colour = model), size = 1.5
  ) +
  annotation_north_arrow(
    location = "tr", which_north = "true",
    style = north_arrow_fancy_orienteering,
    height = unit(10, "mm"),
    width = unit(10, "mm")
  ) +
  annotation_scale(
    location = "br",
    text_cex = 3
  ) +
  scale_shape_manual(values = c(1, 2)) +
  scale_colour_manual(values = c("black", "red")) +
  theme_nothing() +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  labs(x = NULL, y = NULL) +
  theme(
    panel.border = element_rect(color = "black", fill = NA, size = .5),
    text = element_text(size = 42)
  )

inset

# make full plot
base <- ggplot(data = worldmap) +
  geom_sf(
    fill = "#c4cfc8",
    color = "#4b4949d0",
    lwd = 0.2
  ) +
  coord_sf(
    xlim = c(-11, 15),
    ylim = c(49.5, 60)
  ) +
  geom_point(data = map_data, aes(x = lon, y = lat), size = 0.2) +
  geom_rect(
    aes(
      xmin = points_area$xmin[[1]] - 0.1, xmax = points_area$xmax[[1]] + 0.1,
      ymin = points_area$ymin[[1]] - 0.1, ymax = points_area$ymax[[1]] + 0.1
    ),
    fill = NA,
    colour = "black",
    size = .02
  ) +
  theme_nothing() +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  labs(x = NULL, y = NULL) +
  theme(
    panel.border = element_rect(color = "black", fill = NA, size = .5)
  )

merge_plot <- base +
  annotation_custom(
    ggplotGrob(inset),
    xmin = 1,
    xmax = 13,
    ymin = 52.5,
    ymax = 60
  )

merge_plot

ggsave(
  plot = merge_plot,
  filename = "results/figures/site_map.png",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 300
)


sites_model_plot <- plot_grid(
  all_sites$STU$fit, all_sites$BUR$fit, aic_plot, ks_plot_dist,
  labels = c("A", "B", "C", "D"), label_size = 42
)

sites_model_plot

ggsave(
  plot = sites_model_plot,
  filename = "results/figures/results_model_plot.png",
  width = 183,
  height = 190,
  units = "mm",
  dpi = 300
)
```
